Research has been ongoing to determine ways to reduce the time, manpower, and amounts of fuel required to measure standard ASTM specification properties. These properties are not only useful for on-site fuel diagnostics, but are also vital to overall fuel certification procedures. The end goal has typically been envisioned as a stand-alone tool capable of predicting multiple fuel performance properties using only a single small fuel sample subjected to a single analytical technique. Previous research efforts have typically focused upon analytical techniques that do not provide direct information regarding chemical composition, such as near-infrared (NIR) spectroscopic data. These efforts were successful, resulting in the development of a stand-alone prototype instrument, which predicted various critical specification fuel properties using the partial least squares (PLS) modeling of NIR spectroscopic data. The instrument was primarily trained on and applied to standard petrochemical fuels, but was also adapted to accommodate the accurate analysis of Fischer-Tropsch (FT) synthetic fuels, fuels derived from biomass, and blends of these two fuel types with petrochemical fuels, as well as the accurate identification of ultra-low sulfur diesel (ULSD) fuels.
Other research is also generally focused on development of predicative models based on spectroscopic, and especially NIR, data. This focus exists for many reasons, including the relative ease with which such data can be collected and accommodated for chemometric model development and subsequent stand-alone tool development. However, research has shown that adapting NIR property prediction models based on petrochemical fuels to properly accommodate non-petrochemical fuels generally requires a devoted research effort. In a best-case scenario, models must be adapted to each possible alternative fuel type; while in a worst-case scenario, models must be redeveloped for each individual fuel within a type. Critically, there is an inherent unpredictability in both the type and quantity of non-petrochemical fuels that might exist in worldwide fuel populations for the near future. Therefore, it was determined that the accommodation of non-petrochemical fuels could not be realistically performed on a case-by-case basis. Although some strategies have been developed to improve NIR fuel property modeling algorithms in an automated fashion to enhance the performance of the previously developed stand-alone prototype instrument, these were discrete, incremental modeling improvements that are insufficient to address the fundamental challenge presented by uncalibrated fuel types.
Accordingly, there remains a need in the art for a new analysis strategy based on a more granular, chemical-by-chemical assessment of fuel composition than is possible with spectroscopic methods, such as NIR, to accommodate the unpredictable nature of future fuels. This strategy would concurrently address a long-standing concern of practical fuel implementation, true fit-for-purpose (FFP) fuel modeling based on fuel composition. A great deal of information regarding fuel composition, and even fuel performance, can already be obtained from GC-MS data, which makes it an ideal analytical technique upon which to base the in-depth and universally-applicable fuel analysis strategy required for the desired software tool.